Study on Improved Building Energy Consumption Prediction Models Based on Genetic Algorithm
As a pillar industry of the national economy,the construction industry consumes a large amount of en-ergy while promoting social development,accelerating the energy conservation and emission reduction in the con-struction industry is of great significance to realize China's energy conservation and emission reduction target of achieving peak CO2 emissions in 2030.Therefore,this paper analyzes the building energy consumption of univer-sities with relatively concentrated personnel,and through collecting a large number of building energy consump-tion data on campus,fully excavates the characteristics of building energy consumption,and optimizes the BP and LSTM building energy consumption prediction models with genetic algorithm,through experimental comparison,the most suitable prediction model on campus is selected to predict the future use of building energy consumption on campus,and provide basic data for the subsequent carbon emission prediction and energy saving measures on campus.Through experimental comparison,it can be found that compared with the GA-LSTM model,the GA-BP model has a mean absolute error,mean square error,root mean square error,and mean absolute percentage error reduction of 8.83%,4.80%,2.43%,and 10.2%,respectively,the GA-BP model has smaller errors and higher de-gree of fitting,which is more realistic for the prediction of building energy consumption in universities,and can be used for the prediction of energy consumption in universities.
genetic algorithmGA-BP modelbuilding energy consumptionbuilding energy conservation
NETL
NSTL
万方数据
